A machine learning analysis of risk and protective factors of suicidal thoughts and behaviors in college students.

OBJECTIVE: To identify robust and reproducible factors associated with suicidal thoughts and behaviors (STBs) in college students. METHODS: 356 first-year university students completed a large battery of demographic and clinically-relevant self-report measures during the first semester of college and end-of-year (n = 228). Suicide Behaviors Questionnaire-Revised (SBQ-R) assessed STBs. A machine learning (ML) pipeline using stacking and nested cross-validation examined correlates of SBQ-R scores. RESULTS: 9.6% of students were identified at significant STBs risk by the SBQ-R. The ML algorithm explained 28.3% of variance (95%CI: 28-28.5%) in baseline SBQ-R scores, with depression severity, social isolation, meaning and purpose in life, and positive affect among the most important factors. There was a significant reduction in STBs at end-of-year with only 1.8% of students identified at significant risk. CONCLUSION: Analyses replicated known factors associated with STBs during the first semester of college and identified novel, potentially modifiable factors including positive affect and social connectedness.

Rapid, reliable mobile assessment of affect-related motor processing.

Mobile technologies can be used for behavioral assessments to associate changes in behavior with environmental context and its influence on mental health and disease. Research on real-time motor control with a joystick, analyzed using a computational proportion-derivative (PD) modeling approach, has shown that model parameters can be estimated with high reliability and are related both to self-reported fear and to brain structures important for affective regulation, such as the anterior cingulate cortex. Here we introduce a mobile version of this paradigm, the rapid assessment of motor processing (RAMP) paradigm, and show that it provides robust, reliable, and accessible behavioral measurements relevant to mental health. A smartphone version of a previous joystick sensorimotor task was developed in which participants control a virtual car to a stop sign and stop. A sample of 89 adults performed the task, with 66 completing a second retest session. A PD modeling approach was applied to compute Kp (drive) and Kd (damping) parameters. Both Kp and Kd exhibited high test-retest reliabilities (ICC .81 and .78, respectively). Replicating a previous finding from a different sample with the joystick version of the task, both Kp and Kd were negatively associated with self-reported fear. The RAMP paradigm, a mobile sensorimotor assessment, can be used to assess drive and damping during motor control, which is robustly associated with subjective affect. This paradigm could be useful for examining dynamic contextual modulation of affect-related processing, which could improve assessment of the effects of interventions for psychiatric disorders in a real-world context.

Striatal reactivity during emotion and reward relates to approach-avoidance conflict behaviour and is altered in adults with anxiety or depression.

BACKGROUND: We have previously reported activation in reward, salience and executive control regions during functional MRI (fMRI) using an approach-avoidance conflict (AAC) decision-making task with healthy adults. Further investigations into how anxiety and depressive disorders relate to differences in neural responses during AAC can inform their understanding and treatment. We tested the hypothesis that people with anxiety or depression have altered neural activation during AAC. METHODS: We compared 118 treatment-seeking adults with anxiety or depression and 58 healthy adults using linear mixed-effects models to examine group-level differences in neural activation (fMRI) during AAC decision-making. Correlational analyses examined relationships between behavioural and neural measures. RESULTS: Adults with anxiety or depression had greater striatal engagement when reacting to affective stimuli (p = 0.008, d = 0.31) regardless of valence, and weaker striatal engagement during reward feedback (p = 0.046, d = -0.27) regardless of the presence of monetary reward. They also had blunted amygdala activity during decision-making (p = 0.023, d = -0.32) regardless of the presence of conflict. Across groups, approach behaviour during conflict decision-making was inversely correlated with striatal activation during affective stimuli (p < 0.001, r = -0.28) and positively related to striatal activation during reward feedback (p < 0.001, r = 0.27). LIMITATIONS: Our transdiagnostic approach did not allow for comparisons between specific anxiety disorders, and our cross-sectional approach did not allow for causal inference. CONCLUSION: Anxiety and depression were associated with altered neural responses to AAC. Findings were consistent with the role of the striatum in action selection and reward responsivity, and they point toward striatal reactivity as a future treatment target. Blunting of amygdala activity in anxiety or depression may indicate a compensatory response to inhibit affective salience and maintain approach.

Behavioral and neural responses during fear conditioning and extinction in a large transdiagnostic sample.

BACKGROUND: Dysregulation of fear learning has been associated with psychiatric disorders that have altered positive and negative valence domain function. While amygdala-insula-prefrontal circuitry is considered important for fear learning, there have been inconsistencies in neural findings in healthy and clinical human samples. This study aimed to delineate the neural substrates and behavioral responses during fear learning in a large, transdiagnostic sample with predominantly depressive and/or anxious dysfunction. METHODS: Two-hundred and eighty-two individuals (52 healthy participants; 230 participants with depression and/or anxiety-related problems) from the Tulsa 1000 study, an ongoing, naturalistic longitudinal study based on a dimensional psychopathological framework, completed a Pavlovian fear learning task during functional magnetic resonance imaging. Linear mixed-effects analyses examined condition-by-time effects on brain activation (CS+, CS- across familiarization, conditioning, and extinction trials). A data-driven latent profile analysis (LPA) examined distinct patterns of behavioral and neural responses to threat across fear conditioning and extinction, while logistic regression analyses evaluated cognitive-affective predictors of latent profiles. RESULTS: Whole-brain analyses revealed a condition-by-time interaction in the anterior insula, postcentral gyrus, superior temporal gyrus, middle frontal gyrus, and cerebellum but not amygdala. The LPA identified distinct latent profiles across subjective and neural levels of measurement. Anterior insula profiles were characterized by marginal differences in age and state anxiety. CONCLUSIONS: Our findings demonstrate that human fear learning recruits a distributed network of regions involved in interoceptive, cognitive, motivational, and psychomotor processes. Data-driven analyses identified distinct profiles of subjective and neural responses during fear learning that transcended clinical diagnoses, but no robust relationships to demographic or cognitive-affective variable were identified.

P300 amplitude during a monetary incentive delay task predicts future therapy completion in individuals with major depressive disorder.

INTRODUCTION: Treatment effectiveness for major depressive disorder (MDD) is often affected by client non-adherence, dropout, and non-response. Identification of client characteristics predicting successful treatment completion and/or response (i.e., symptom reduction) may be an important tool to increase intervention effectiveness. It is unclear whether neural attenuations in reward processing associated with MDD predict behavioral treatment outcome. METHODS: This study aimed to determine whether blunted neural responses to reward at baseline differentiate MDD (n = 60; 41 with comorbid anxiety) and healthy control (HC; n = 40) groups; and predict MDD completion of and response to 7-10 sessions of behavior therapy. Participants completed a monetary incentive delay (MID) task. The N200, P300, contingent negative variation (CNV) event related potentials (ERPs) and behavioral responses (reaction time [RT], correct hits) were quantified and extracted for cross-sectional group analyses. ERPs and behavioral responses demonstrating group differences were then used to predict therapy completion and response within MDD. RESULTS: MDD exhibited faster RT and smaller P300 amplitudes than HC across conditions. Within the MDD group, treatment completers (n = 37) exhibited larger P300 amplitudes than non-completers (n = 21). LIMITATIONS: This study comprises secondary analyses of EEG data; thus task parameters are not optimized to examine feedback ERPs from the paradigm. We did not examine heterogenous presentations of MDD; however, severity and comorbidity did not influence findings. CONCLUSIONS: Previous studies suggest that P300 is an index of motivational salience and stimulus resource allocation. In sum, individuals who deploy greater neural resources to task demands are more likely to persevere in behavioral therapy.

Common Data Elements, Scalable Data Management Infrastructure, and Analytics Workflows for Large-Scale Neuroimaging Studies.

Neuroscience studies require considerable bioinformatic support and expertise. Numerous high-dimensional and multimodal datasets must be preprocessed and integrated to create robust and reproducible analysis pipelines. We describe a common data elements and scalable data management infrastructure that allows multiple analytics workflows to facilitate preprocessing, analysis and sharing of large-scale multi-level data. The process uses the Brain Imaging Data Structure (BIDS) format and supports MRI, fMRI, EEG, clinical, and laboratory data. The infrastructure provides support for other datasets such as Fitbit and flexibility for developers to customize the integration of new types of data. Exemplar results from 200+ participants and 11 different pipelines demonstrate the utility of the infrastructure.

Long-term stability of computational parameters during approach-avoidance conflict in a transdiagnostic psychiatric patient sample.

Maladaptive behavior during approach-avoidance conflict (AAC) is common to multiple psychiatric disorders. Using computational modeling, we previously reported that individuals with depression, anxiety, and substance use disorders (DEP/ANX; SUDs) exhibited differences in decision uncertainty and sensitivity to negative outcomes versus reward (emotional conflict) relative to healthy controls (HCs). However, it remains unknown whether these computational parameters and group differences are stable over time. We analyzed 1-year follow-up data from a subset of the same participants (N = 325) to assess parameter stability and relationships to other clinical and task measures. We assessed group differences in the entire sample as well as a subset matched for age and IQ across HCs (N = 48), SUDs (N = 29), and DEP/ANX (N = 121). We also assessed 2-3 week reliability in a separate sample of 30 HCs. Emotional conflict and decision uncertainty parameters showed moderate 1-year intra-class correlations (.52 and .46, respectively) and moderate to excellent correlations over the shorter period (.84 and .54, respectively). Similar to previous baseline findings, parameters correlated with multiple response time measures (ps < .001) and self-reported anxiety (r = .30, p < .001) and decision difficulty (r = .44, p < .001). Linear mixed effects analyses revealed that patients remained higher in decision uncertainty (SUDs, p = .009) and lower in emotional conflict (SUDs, p = .004, DEP/ANX, p = .02) relative to HCs. This computational modelling approach may therefore offer relatively stable markers of transdiagnostic psychopathology.

Test-retest reliability of approach-avoidance conflict decision-making during functional magnetic resonance imaging in healthy adults.

Neural and behavioral mechanisms during approach-avoidance conflict decision-making are relevant across various psychiatric disorders, particularly anxiety disorders. Studies using approach-avoidance conflict paradigms in healthy adults have identified preliminary neural mechanisms, but findings must be replicated and demonstrated as reliable before further application. This study sought to replicate previous findings and examine test-retest reliability of behavioral (approach behavior, reaction time) and neural (regions of interest [ROIs]) responses during an approach-avoidance conflict task conducted during functional magnetic resonance imaging (fMRI). Thirty healthy adults completed an approach-avoidance conflict task during fMRI on two occasions (mean interval: 17 days; range: 11-32). Effects of task condition during three task phases (decision-making, affective outcome and monetary reward) and intraclass correlation coefficients (ICCs) were calculated across time points. Results replicated that approach behavior was modulated by conflict during decision-making. ROI activations were replicated such that dorsal anterior cingulate cortex (dACC) was modulated by conflict during decision-making, and dACC, striatum, and anterior insula were modulated by valence during affective outcomes (p's <.0083). Approach behavior during conflict demonstrated excellent reliability (ICCs ≥.77). Activation of dACC during conflict decision-making and anterior insula during negative outcomes demonstrated fair reliability (ICCs = .51 and .54), and dACC and striatum activation demonstrated good reliability during negative outcomes (ICCs = .63 and .69). Two additional ROIs (amygdala, left dorsolateral prefrontal cortex) showed good reliability during negative outcomes (ICCs ≥.60). These results characterize several specific behavioral and neuroimaging responses that are replicable and sufficiently reliable during approach-avoidance conflict decision-making to support future utility.

Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach.

BACKGROUND: Imbalances in approach-avoidance conflict (AAC) decision-making (e.g., sacrificing rewards to avoid negative outcomes) are considered central to multiple psychiatric disorders. We used computational modelling to examine 2 factors that are often not distinguished in descriptive analyses of AAC: decision uncertainty and sensitivity to negative outcomes versus rewards (emotional conflict). METHODS: A previously validated AAC task was completed by 478 participants, including healthy controls (n = 59), people with substance use disorders (n = 159) and people with depression and/or anxiety disorders who did not have substance use disorders (n = 260). Using an active inference model, we estimated individual-level values for a model parameter that reflected decision uncertainty and another that reflected emotional conflict. We also repeated analyses in a subsample (59 healthy controls, 161 people with depression and/or anxiety disorders, 56 people with substance use disorders) that was propensity-matched for age and general intelligence. RESULTS: The model showed high accuracy (72%). As further validation, parameters correlated with reaction times and self-reported task motivations in expected directions. The emotional conflict parameter further correlated with self-reported anxiety during the task (r = 0.32, p < 0.001), and the decision uncertainty parameter correlated with self-reported difficulty making decisions (r = 0.45, p < 0.001). Compared to healthy controls, people with depression and/or anxiety disorders and people with substance use disorders showed higher decision uncertainty in the propensity-matched sample (t = 2.16, p = 0.03, and t = 2.88, p = 0.005, respectively), with analogous results in the full sample; people with substance use disorders also showed lower emotional conflict in the full sample (t = 3.17, p = 0.002). LIMITATIONS: This study was limited by heterogeneity of the clinical sample and an inability to examine learning. CONCLUSION: These results suggest that reduced confidence in how to act, rather than increased emotional conflict, may explain maladaptive approach-avoidance behaviours in people with psychiatric disorders.

Visual cortical regions show sufficient test-retest reliability while salience regions are unreliable during emotional face processing.

Functional magnetic resonance imaging studies frequently use emotional face processing tasks to probe neural circuitry related to psychiatric disorders and treatments with an emphasis on regions within the salience network (e.g., amygdala). Findings across previous test-retest reliability studies of emotional face processing have shown high variability, potentially due to differences in data analytic approaches. The present study comprehensively examined the test-retest reliability of an emotional faces task utilizing multiple approaches to region of interest (ROI) analysis and by examining voxel-wise reliability across the entire brain for both neural activation and functional connectivity. Analyses included 42 healthy adult participants who completed an fMRI scan concurrent with an emotional faces task on two separate days with an average of 25.52 days between scans. Intraclass correlation coefficients (ICCs) were calculated for the 'FACES-SHAPES' and 'FACES' (compared to implicit baseline) contrasts across the following: anatomical ROIs identified from a publicly available brain atlas (i.e., Brainnetome), functional ROIs consisting of 5-mm spheres centered on peak voxels from a publicly available meta-analytic database (i.e., Neurosynth), and whole-brain, voxel-wise analysis. Whole-brain, voxel-wise analyses of functional connectivity were also conducted using both anatomical and functional seed ROIs. While group-averaged neural activation maps were consistent across time, only one anatomical ROI and two functional ROIs showed good or excellent individual-level reliability for neural activation. The anatomical ROI was the right medioventral fusiform gyrus for the FACES contrast (ICC â€‹= â€‹0.60). The functional ROIs were the left and the right fusiform face area (FFA) for both FACES-SHAPES and FACES (Left FFA ICCs â€‹= â€‹0.69 & 0.79; Right FFA ICCs â€‹= â€‹0.68 & 0.66). Poor reliability (ICCs â€‹< â€‹0.4) was identified for almost all other anatomical and functional ROIs, with some exceptions showing fair reliability (ICCs â€‹= â€‹0.4-0.59). Whole-brain voxel-wise analysis of neural activation identified voxels with good (ICCs â€‹= â€‹0.6-0.74) to excellent reliability (ICCs â€‹> â€‹0.75) that were primarily located in visual cortex, with several clusters in bilateral dorsal lateral prefrontal cortex (DLPFC). Whole-brain voxel-wise analyses of functional connectivity for amygdala and fusiform gyrus identified very few voxels with good to excellent reliability using both anatomical and functional seed ROIs. Exceptions included clusters in right cerebellum and right DLPFC that showed reliable connectivity with left amygdala (ICCs â€‹> â€‹0.6). In conclusion, results indicate that visual cortical regions demonstrate good reliability at the individual level for neural activation, but reliability is generally poor for salience regions often focused on within psychiatric research (e.g., amygdala). Given these findings, future clinical neuroimaging studies using emotional faces tasks to examine individual differences might instead focus on visual regions and their role in psychiatric disorders.

Web-Based Graphic Representation of the Life Course of Mental Health: Cross-Sectional Study Across the Spectrum of Mood, Anxiety, Eating, and Substance Use Disorders.

BACKGROUND: Although patient history is essential for informing mental health assessment, diagnosis, and prognosis, there is a dearth of standardized instruments measuring time-dependent factors relevant to psychiatric disorders. Previous research has demonstrated the potential utility of graphical representations, termed life charts, for depicting the complexity of the course of mental illness. However, the implementation of these assessments is limited by the exclusive focus on specific mental illnesses (ie, bipolar disorder) and the lack of intuitive graphical interfaces for data collection and visualization. OBJECTIVE: This study aimed to develop and test the utility of the Tulsa Life Chart (TLC) as a Web-based, structured approach for obtaining and graphically representing historical information on psychosocial and mental health events relevant across a spectrum of psychiatric disorders. METHODS: The TLC interview was completed at baseline by 499 participants of the Tulsa 1000, a longitudinal study of individuals with depressive, anxiety, substance use, or eating disorders and healthy comparisons (HCs). All data were entered electronically, and a 1-page electronic and interactive graphical representation was developed using the Google Visualization Application Programming Interface. For 8 distinct life epochs (periods of approximately 5-10 years), the TLC assessed the following factors: school attendance, hobbies, jobs, social support, substance use, mental health treatment, family structure changes, negative and positive events, and epoch and event-related mood ratings. We used generalized linear mixed models (GLMMs) to evaluate trajectories of each domain over time and by sex, age, and diagnosis, using case examples and Web-based interactive graphs to visualize data. RESULTS: GLMM analyses revealed main or interaction effects of epoch and diagnosis for all domains. Epoch by diagnosis interactions were identified for mood ratings and the number of negative-versus-positive events (all P values <.001), with all psychiatric groups reporting worse mood and greater negative-versus-positive events than HCs. These differences were most robust at different epochs, depending on diagnosis. There were also diagnosis and epoch main effects for substance use, mental health treatment received, social support, and hobbies (P<.001). User experience ratings (each on a 1-5 scale) revealed that participants found the TLC pleasant to complete (mean 3.07, SD 1.26) and useful for understanding their mental health (mean 3.07, SD 1.26), and that they were likely to recommend it to others (mean 3.42, SD 0.85). CONCLUSIONS: The TLC provides a structured, Web-based transdiagnostic assessment of psychosocial history relevant for the diagnosis and treatment of psychiatric disorders. Interactive, 1-page graphical representations of the TLC allow for the efficient communication of historical life information that would be useful for clinicians, patients, and family members.